Micheline Beaulieu

27 total papers · 826 total citations
20 papers, 585 citations indexed

About

Micheline Beaulieu is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Micheline Beaulieu has authored 20 papers receiving a total of 585 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Information Systems, 9 papers in Artificial Intelligence and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Micheline Beaulieu's work include Information Retrieval and Search Behavior (13 papers), Semantic Web and Ontologies (5 papers) and Expert finding and Q&A systems (5 papers). Micheline Beaulieu is often cited by papers focused on Information Retrieval and Search Behavior (13 papers), Semantic Web and Ontologies (5 papers) and Expert finding and Q&A systems (5 papers). Micheline Beaulieu collaborates with scholars based in United Kingdom, United States and Sweden. Micheline Beaulieu's co-authors include Sung Hyon Myaeng, Ricardo Baeza‐Yates, Kalervo Järvelin, Stephen Robertson, Edie Rasmussen, Mark Sanderson, Susan Jones, Daniela Petrelli, Hideo Joho and Christine L. Borgman and has published in prestigious journals such as Scientometrics, Journal of Documentation and Interacting with Computers.

In The Last Decade

Micheline Beaulieu

20 papers receiving 538 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Micheline Beaulieu 364 329 91 55 49 20 585
Bert R. Boyce 285 0.8× 282 0.9× 65 0.7× 43 0.8× 32 0.7× 41 595
Dmitri Roussinov 248 0.7× 367 1.1× 100 1.1× 52 0.9× 50 1.0× 48 622
Jack Muramatsu 356 1.0× 273 0.8× 74 0.8× 75 1.4× 29 0.6× 8 533
Richard Orwig 168 0.5× 262 0.8× 108 1.2× 48 0.9× 28 0.6× 14 497
Dan Liebling 349 1.0× 354 1.1× 59 0.6× 42 0.8× 50 1.0× 9 681
Amin Mantrach 238 0.7× 285 0.9× 98 1.1× 40 0.7× 21 0.4× 19 545
J. Mills 317 0.9× 290 0.9× 68 0.7× 49 0.9× 31 0.6× 12 541
Daniel J. Liebling 299 0.8× 169 0.5× 85 0.9× 45 0.8× 56 1.1× 14 511
Peter Anick 347 1.0× 439 1.3× 72 0.8× 101 1.8× 35 0.7× 30 693
Jae‐wook Ahn 279 0.8× 187 0.6× 232 2.5× 52 0.9× 37 0.8× 34 524

Countries citing papers authored by Micheline Beaulieu

Since Specialization
Citations

This map shows the geographic impact of Micheline Beaulieu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Micheline Beaulieu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Micheline Beaulieu more than expected).

Fields of papers citing papers by Micheline Beaulieu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Micheline Beaulieu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Micheline Beaulieu. The network helps show where Micheline Beaulieu may publish in the future.

Co-authorship network of co-authors of Micheline Beaulieu

This figure shows the co-authorship network connecting the top 25 collaborators of Micheline Beaulieu. A scholar is included among the top collaborators of Micheline Beaulieu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Micheline Beaulieu. Micheline Beaulieu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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